# -*- coding:utf-8 -*-
# dataset settings
# vaihigenIRRG -> potsdamIRRG
dataset_type = 'PotsdamDataset'
data_root = 'data'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)

# --------------------source domain pipline---------------------------
# --------------------vaihigen pipline------------------------------
vaihigen_train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='Resize', img_scale=(512, 512), ratio_range=(1.0, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]

# --------------------target domain pipline---------------------------
# --------------------potsdamIRRG pipline------------------------------
potsdam_train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='Resize', img_scale=(512, 512), ratio_range=(1.0, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(512, 512),
        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=4,
    train=dict(
        type='UDADataset',
        source=dict(
            type='ISPRSDataset',
            data_root=data_root,
            img_dir='Vaihingen_IRRG_DA/img_dir/train',
            ann_dir='Vaihingen_IRRG_DA/ann_dir/train',
            split='Vaihingen_IRRG_DA/train.txt',
            pipeline=vaihigen_train_pipeline),
        target=dict(
            type=dataset_type,
            data_root=data_root,
            img_dir='Potsdam_IRRG_DA/img_dir/train',
            ann_dir='Potsdam_IRRG_DA/ann_dir/train',
            split='Potsdam_IRRG_DA/train.txt',
            pipeline=potsdam_train_pipeline)),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='Potsdam_IRRG_DA/img_dir/val',
        ann_dir='Potsdam_IRRG_DA/ann_dir/val',
        split='Potsdam_IRRG_DA/val.txt',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='Potsdam_IRRG_DA/img_dir/val',
        ann_dir='Potsdam_IRRG_DA/ann_dir/val',
        split='Potsdam_IRRG_DA/val.txt',
        pipeline=test_pipeline))
